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Which Open Source Video Apps Use SMP Effectively?
ydrol writes "After building my new Core 2 Quad Q6600 PC, I was ready to unleash video conversion activity the likes of which I had not seen before. However, I was disappointed to discover that a lot of the conversion tools either don't use SMP at all, or don't balance the workload evenly across processors, or require ugly hacks to use SMP (e.g. invoking distributed encoding options). I get the impression that open source projects are a bit slow on the uptake here? Which open source video conversion apps take full native advantage of SMP? (And before you ask, no, I don't want to pick up the code and add SMP support myself, thanks.)"
Use the -threads switch.
Interested in open source engine management for your Subaru?
transocde uses separate processes for everything.
My blog
x264 use slices and scales pretty well across multiple cores. I use it on windows via megui, but you could easily use it in Linux as well. You could use mencoder to pipe out raw video to a fifo and use x264 to do the actual conversion, for instance.
...makes excellent use of multiple cores. It is however Mac-only. Interestingly, what it does is split a file into chunks and spawns multiple ffmpeg processes to do the conversion. Which is to say, perhaps you can do some (relatively simple) scripting with ffmpeg that will do the job.
The secret to creativity is knowing how to hide your sources. - Albert Einstein
x264 via meGUI from Doom9 is what I use to compress HD-DVD and BD movies - also on a quad core. I have some tutorials posted out and about on how I'm doing it. Near as I can tell you cannot dupe the process on Linux due to the crypto - Slysoft's AnyDVD-HD is needed.
Playback - I use XBMC for Linux. It is also SMP enabled using the ffmpeg cabac patch. the developers of this project have been VERY aggressive at taking cutting edge improvements to the likes of ffmpeg and incorporating them into the code. Since Linux has no video acceleration of H.264 SMP really helps on high bitrate video!
Build it, Drive it, Improve it! Hybridz.org
don't balance the workload evenly across processors
Why is balancing the load evenly important, as long as one thread is not bottlenecking the others? Loading a particular core or set of cores might even be beneficial depending on the cache implementation, especially when other applications are also contending for CPU time.
Sure, a nice even load distribution might be an indicator for good design, but it doesn't have to apply in every case. I don't think software should be designed so you can be pleased with the aesthetics of the charts in task manager.
Handbrake has always used both of the cores on my system for transcoding.
OP is asking for open source tools. You cited a commercial one that doesn't provide source.
VisualHub (the front-end app) may be closed, but ffmpeg is LGPL.
And the GP was suggesting using ffmpeg, not VisualHub.
How can I believe you when you tell me what I don't want to hear?
The problem with MPEG encoding and decoding is that the data itself is not well suited to multi-threaded analysis.
Multi-threading is most efficient when it is applied to discrete data sets that have little or no dependency on each other.
For example, suppose I have a table with four columns -- three holding input values (A, B, and C) and one holding an output value (X). If the data in a given row of the table has nothing to do with the data in any other row, multi-threading works efficiently, because none of the threads are waiting for data from any of the other threads. If I want to process multiple rows at once, I simply spawn additional threads.
On the other hand, for data such as MPEG video, the composition of the next frame is equal to the composition of the current frame, plus some delta transformation - the changed pixels.
This introduces a dependency which precludes efficient multi-threaded processing, because each succeeding frame depends on the output of the calculations used to generate the prior frame. Even if more than one core is dedicated to processing the video stream, one core would wind up waiting on another, because the output from the first core would be used as the input to the second.
Running multiple instances of the same code concurrently in multiple threads is simple. Even running mutually exclusive parts of the same code concurrently in separate threads is easy. Converting complex serial algorithms to effectively utilize multiple cores is generally not simple. And writing code that can scale and balance across n number of cores/threads is extremely hard. There are all sorts of synchronization issues to deal with, scheduling issues, data transport issues, etc.. and it becomes increasingly hard to debug code the more cores/threads you throw in. I think the stigma is justified.
Actually, the MPEG stream resets itself every n frames or so (n is often a number like 8, but can vary depending on the video content). These are called keyframes (K) and the delta frames (called P and I frames) are generated against them. Because of this, it is really easy to apply parallel processing to video encoding.
You don't say if you're running on Windows or Linux or something else. If you are running on Windows, the latest versions of VirtualDub have made big improvements to SMT/SMP encoding.
VirtualDub home
VirtualDub 1.8.1 announcement
VirtualDub downloads
Make sure you grab 1.8.3 - 1.8.1 was pretty good, but had a few teething problems. 1.8.2 has a major regression which is fixed in 1.8.3. The comments in the 1.8.1 announcement contain a few important tips for using the new features (some of which I posted BTW).
The two major new features that would be of interest to you are:
1. You can run all VirtualDub processing in one thread, and the codec in another. This works very well in conjunction with a multi-threaded codec - this one change improved my CPU utilitisation from approx 75% to 95% on my dual-core machines - with an equivalent increase in encoding performance.
2. VD now has simple support for distributed encoding. You can use a shared queue across either multiple instances of VD on a single machine, or across multiple machines (must use UNC paths for multiple machines). Each instance of VD will pick the next job in the queue when it finishes its current job. Instances can be started in slave mode (in which case they will automatically start processing the queue).
I use 3 machines for encoding (all dual-core). With VD 1.8.x I start VD on two of the machines in slave mode, and one in master mode. I add jobs to the queue on the master instance, and the other two instances immediately pick up the new jobs and start encoding. When I've added all the jobs, I then start the master instance working on the job queue.
To achieve a similar effect on your quad-code, start two instances of VD on the same machine - one slave, the other master.
It's not perfect (if you've only got one job, you won't use your maximum capacity) but it has greatly simplified my transcoding tasks, and reduced the time to transcode large numbers of files.
I've noticed a lot of talk about commandline options, but not the nice guis that use them. Avidemux is open source, cross-platform, gives you a decent interface, and uses multithreaded libraries like ffmpeg and x264 on the backend to do the encoding, so it generally makes optimal use of your multicore system.
-- sudo.ca
Exactly. Too many people assume that any given programmer can write any given program. What isn't generally realized (at least by the masses) is that programming really is about acquiring expertise in a particular domain and then solving problems in that domain through the use of computer programs. Generally some of the most effective programs I've seen have been written, on their first pass, by a person with intimate domain knowledge, and mediocre programming/computer knowledge. The program then becomes a standout when someone with intense programming and computer architecture knowledge improves the code from there (they need not be a subject domain expert, but it helps).
I do take issue with sexconker assuming that I "just don't get it". Heh. If s/he only knew. Whatever, no biggie. I do agree that distributed algorithms are generally more difficult to implement/design than non-distributed, but that's not exactly the same thing as serial versus parallel algorithms (non-distributed generally involves access to data through a common address space, distributed doesn't, though even those pseudo-definitions come up a bit short).
Again and again I read in industry rags and on various web sites that multi-threaded programming is hard, and nobody knows how to do it, and that it's difficult to debug, and all that. I believe what they're really saying is "The set of programmers who are accustomed to multi-threaded programming/debugging is (relatively) small, and thus applications aren't going to make good use of the shift to multicore CPU packages." Familiarity with a skill, and the supply of labor familiar with said skill, is distinct from it being easy or hard.
Anyway, I stand by my belief that parallel programming is not as difficult as most people are led to believe. Some problems don't lend themselves well to parallel solutions, or don't merit the added complexity, but many many of them do. In ten years time I predict that most computer programming education will assume the use of threading, and that anyone who isn't competent with threading will severely limit their own job prospects.
Cyrano de Maniac
As other commenters have said, decoding video is not, per se, a trivially parallelized algorithm. Especially for modern codecs with lots of temporal encoding. MJPEG would be easily parallelized, buy you'd have to be dealing with fairly ancient sources...MediaComposer 1 for instance.
However, there are different classes of "video app" that are good targets for parallelization. Real world video editing for instance: consider multiple streams of video with overlays, rotations, effects etc. Video and audio decoding can happen in parallel, you can pipeline the effects stages so that each effect is handed off to another core. Modern video editing systems do this with aplomb.
I'm from the commercial end of this so, I can't comment much on open source alternatives. But I will say that a lot of the algorithms in certain products are highly tuned to the particular CPU type.
And they're smart enough to distribute work across only as many cores as actually exist.
Finally. Don't forget that optimization is hard. You have to consider the speed of the hard drive, the cost of sharing data between threads and cpu caches and a bunch of other real constraints. Any half decent cpu of the last five years or so can easily decode most video faster than it can be read and written to disk. So long as this is true, you won't get any benefit from parallelization.
But Mac users have been living with SMP since 2001
Just for reference:
UNIX System V R4-MP 1993
Windows NT 1993
OS/2 2.11 1993
Linux 2.0 1996